More recent attention has focused on the changing nature of the HIV epidemic in the United States. Specifically, there are increasing concerns that specific populations are being disproportionately affected by HIV. These populations include African Americans (AA) and Latino sexual minorities, transgender populations, AA heterosexual women, and people living in the southern and rural communities. Evidence suggests that Pre-Exposure Prophylaxis (PrEP) use is one of the most effective HIV prevention strategies which can decrease HIV incidence by as much as 90% when adhered to daily. However, rates of adherence to PrEP have been reported to be much lower in AA heterosexual women, who account for approximately 10% of new HIV infections in the United States. In fact, early clinical trials of PrEP in this population have failed to show efficacy, largely due to low adherence. Given that adherence is the principle factor in determining PrEP efficacy, studying barriers to uptake and adherence are warranted in this vulnerable demographic. While the most common reasons for PrEP non-adherence in AA women are reported to be forgetting to take the medication and not having it on hand, these women have also reported a range of barriers to PrEP adherence at the individual, interpersonal, and institutional levels. This project will use big data analysis to conduct formative research at a national level to ensure all sub-communities are represented. Understanding the actual behavior of individuals is important in the development of effective interventions. Using passive measurement techniques and digital phenotyping techniques, we will attempt to learn more about the daily lives of AA women, including digital and traditional media use; when and where they spend most of their time; with whom they associate with and their significant relationships (digital and real-life); and their perceptions of HIV risk, knowledge of PrEP, and access to HIV prevention services. From the digital footprints left by individuals on social media platforms, we can identify temporal and geographic patterns in a wide range of behaviors related to HIV risks, including substance use, commercial sexual behaviors, and unprotected sexual activities. Approaches employed here will utilize social media data in various ways, from using occurrences of specified keywords and topics to identify individuals for follow-up interviews to applying machine-learning techniques to automatically identify words associated with high risk HIV behaviors to detect occurrences of individual instances of those behaviors. To capture detailed patterns of HIV risks behaviors, in real-time, smartphones are the perfect tool. These devices are already imbedded in the daily lives of more than 79% of adult AA women in the US. Ecological momentary assessment (EMA) techniques, which prompt users to respond to a short set of questions, a few times during the day, will be employed to gather fine-grained information about their life-style behaviors, in real-time, on a large scale. In addition, a more passive form of data collection will be employed. We will use smartphone sensors and wearable devices to capture behavioral data without the need for any active input from the user. Crowdsourcing is an ideal approach to capturing snapshots of behaviors, beliefs, and environmental constraints. These platforms will enable us to recruit large numbers of AA women with specific demographic profiles to complete online surveys and experiments. Data from thousands of AA women will be gathered representing the entire US. Using a theory-based approach, we will ask questions about the factors that influence the adoption of and adherence to PrEP, including the advantages and disadvantages of taking PrEP (to identify outcome expectancies, cost-benefits, and underlying behavioral beliefs); normative pressure from significant other; and factors that would facilitate or hinder uptake and adherence to PrEP. The ultimate goal of this project will be to develop and evaluate a digital HIV prevention intervention among AA women that uses passive data collection methodologies and machine learning (e.g., deep learning, AI) to influence adoption and adherence of PrEP.